docstr: Filter and Rank the events based on some heuristics:
  1) Events that are in the falling phase of the queue depth.
  2) Events that have a high idle_time, self_time difference.
  
  Parameters:
      length: The number of events to return.
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code: def rank_events(self, length):
      """
      Filter and Rank the events based on some heuristics:
      1) Events that are in the falling phase of the queue depth.
      2) Events that have a high idle_time, self_time difference.
  
      Parameters:
          length: The number of events to return.
      """
  
      # Find the interval when qd is falling to 0
      import torch
  
      queue_depth_list = list(reversed(self.queue_depth_list))
      qd_values = [e.queue_depth for e in queue_depth_list]
  
      bottom_threashold = 0
      top_threashold = 4
      decrease_interval = []
      i = 0
      while i < len(qd_values):
          if qd_values[i] > bottom_threashold:
              i += 1
              continue
          for j in range(i + 1, len(qd_values)):
              # Find next zero and if the max value between them exceeds
              # the threshold, then we have a falling interval
              next_minimum_idx = index_of_first_match(
                  qd_values, lambda x: x <= bottom_threashold, start=j
              )
              peak_idx = argmax(
                  qd_values, start=j, end=next_minimum_idx
              )
  
              # if is a valid peak, we add to list and continue
              if (
                  peak_idx is not None
                  and qd_values[peak_idx] >= top_threashold
              ):
                  decrease_interval.append(
                      Interval(
                          queue_depth_list[peak_idx].start,
                          queue_depth_list[i].start,
                      )
                  )
                  i = (
                      next_minimum_idx
                      if next_minimum_idx is not None
                      else i
                  )
                  break
          i += 1
      # Filter out events that are not in the decrease interval
      event_list = [
          event
          for event in self.metrics.keys()
          if event.intervals_overlap(decrease_interval)
      ]
      if event_list:
          self_time = torch.tensor(
              [
                  self.metrics[event].self_time_ns
                  for event in event_list
              ],
              dtype=torch.float32,
          )
          idle_time = torch.tensor(
              [
                  self.metrics[event].fraction_idle_time
                  for event in event_list
              ],
              dtype=torch.float32,
          )
          normalized_gain = (
              idle_time - torch.mean(idle_time)
          ) / torch.std(idle_time)
          normalized_self = (
              self_time - torch.mean(self_time)
          ) / torch.std(self_time)
          heuristic_score_list = normalized_gain + 0.6 * normalized_self
  
          # Sort events by heuristic
          event_list = [
              event
              for _, event in sorted(
                  zip(heuristic_score_list, event_list),
                  key=lambda x: x[0],
                  reverse=True,
              )
          ]
          event_list = event_list[:length]
      return event_list
  
imports: import functools
  import re
  from collections import deque
  from dataclasses import dataclass
  from typing import Dict, List
  from torch.autograd import _KinetoEvent
  from torch.autograd.profiler import profile
  from torch.profiler import DeviceType
label_desc: Use image node_id 5 for calling the BasicEvaluation obj's underlying member method code with example usage: obj.rank_events(length) and returns: event_list